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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Control and Calibration of GlueX Central Drift Chamber Using Gaussian Process Regression

The Gluonic Excitations (GlueX) experiment is designed to search for exotic hybrid mesons using photoproduction, and to study the hybrid meson spectrum predicted from Lattice Quantum Chromodynamics. For the first time, the GlueX Central Drift Chamber was controlled autonomously using machine learning (ML) to calibrate in real time while recording cosmic ray tracks. We demonstrate the ability of a Gaussian Process to predict the gain correction calibration factor used to determine a high voltage setting that will stabilize the CDC gain in response to changing environmental conditions; this is in contrast to the traditional, computationally expensive method of calibrating raw data after data collection is complete.

McSpadden, Helen↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Destructive Analysis of TRISO Particles: Crush/Burn/Leach Followed by Davies-Gray Titration and IDMS

The accurate accounting of nuclear materials is a cornerstone of international nuclear safeguards. One emerging challenge in this domain is the fabrication of TRIstructural ISOtropic (TRISO) particle fuels. Although these innovative fuel forms are critical for advanced reactor applications, their robust refractory ceramics and coating compositions present significant obstacles to destructive analysis (DA) methods. Ensuring full and quantitative recovery from these particles is essential for accurate mass accountancy. The current study was initiated to address these challenges, first by validating a previously established destructive method developed by Oak Ridge National Laboratory (ORNL) for the quantitative recovery of uranium from TRISO particles and then following that process with uranium content determination through isotope dilution mass spectrometry (IDMS) and Davies-Gray titration. This study expands on the scope of a digestive method that was developed under the Advanced Gas Reactor Fuel Development and Qualification program and is currently implemented in both the Coated Particle Fuel Development Laboratory and Irradiated Fuels Examination Laboratory at ORNL. The success of the previous Advanced Gas Reactor work relied on developing a DA method to evaluate the fabrication process and reactor experiments. The methodology described in this report was designed to rigorously investigate the efficacy of the crush/burn/leach sample preparation of TRISO particles; it aims to quantify uranium recovery while also assessing the effects of TRISO constituents (e.g., silicon and zirconium) on analytical precision and accuracy. By comparing the results from the titration method and IDMS, we sought to determine whether existing analytical procedures accepted by the International Atomic Energy Agency (IAEA) could be effectively translated to TRISO fuel forms. The team employed an approach that involved processing replicate TRISO samples, optimizing the milling (i.e., crushing) step and performing serial leaches. The elemental composition of the analytical samples was examined to prepare for interference studies in the second year of this project. The integration of gamma spectrometry to verify residual uranium activity further strengthened the validation. Statistical methods were applied to the collected data to evaluate the uncertainties arising from sampling, sample preparation and uranium quantification. These uncertainties were then compared to the IAEA’s international target values (ITVs). Additional data collected in upcoming project work will strengthen the uncertainty estimates. Ultimately, it is hoped that this project will contribute materially to the body of work related to characterization of TRISO based fuels for the purpose of material accountancy and its applications to international safeguards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Teachers’ Beliefs and Their Influence on Math Instructions for Gifted English Learners

The dynamic interplay between teachers’ beliefs and practices significantly impact the quality of instruction and the trajectory of talent development in young students. This case study explores the beliefs and practices of two elementary teachers instructing gifted ELs in mathematics. The constant comparison method was used to analyze data collected from classroom observations, semi-structured interviews, and field notes. Three factors were found to affect the (in)consistency between teachers’ expressed beliefs and observed practices: compatibility among core and peripheral beliefs, knowledge about evidence-based practices, and classroom management skills. Students exhibit higher levels of participation, communication, and engagement in critical thinking skills when their teacher embraces constructive perspectives in teaching mathematics, demonstrates pedagogical expertise, and employs a proactive classroom management approach. Conversely, students encounter restricted opportunities to independently construct their own understanding of mathematics when their teacher holds maladaptive beliefs about teaching mathematics, has limited knowledge of evidence-based practices, and has an authoritarian classroom management style. These findings underscore the need for a new approach to professional development (PD) that encourages teachers to critically examine the connection between their beliefs and instructional practices and their impact on the student’s mathematical talent development.

Yang, Jenny (ORCID:0000000296905466)↗
DOE OSTI2026

Comparison of CNN-Based Image Classification Approaches for Implementation of Low-Cost Multispectral Arcing Detection

Camera-based sensing has benefited in recent years from developments in machine learning data processing methods, as well as improved data collection options such as Unmanned Aerial Vehicles (UAV) mounted sensors. However, cost considerations, both for the initial purchase of sensors as well as updates, maintenance, or potential replacement if damaged, can limit adoption of more expensive sensing options for some applications. To evaluate more affordable options with less expensive, more available, and more easily replaceable hardware, we examine the use of machine learning-based image classification with custom datasets, utilizing deep learning based-image classification and the use of ensemble models for sensor fusion. Utilizing the same models for each camera to reduce technical overhead, we showed that for a very representative training dataset, camera-based detection can be successful for detection of electrical arcing. We also use multiple validation datasets, based on conditions expected to be of varying difficulty, to evaluate custom data. These results show that ensemble models of different data sources can mitigate risks from gaps in training data, though the system will be less redundant for those cases unless other precautions are taken. We found that with good quality custom datasets, data fusion models can be utilized without specialization in design to the specific cameras utilized, allowing for less specialized, more accessible equipment to be utilized as multispectral camera components. This approach can provide an alternative to expensive sensing equipment for applications in which lower-cost or more easily replaceable sensing equipment is desirable.

convolutional neural networks↗
NASA NTRS1988

Technology Developed In Two Space Projects

Two projects examined for their influences on commercial products and future space missions. Three-volume report presents study of technological benefits of two spaceflight projects, Galileo, and Hubble space telescope. Study focused on equipment and computer programs developed between initial approval of projects and integration and testing of the systems. Volume 1 presents overview of study, discusses methods of collection and evaluation of data, and offers conclusion and recommendations. Volume 2 describes interview process and summarizes raw data. Volume 3 contains technology-assessment sheets for developments.

Joseph, Carlton H.↗
NASA NTRS1993

Soft computing in design and manufacturing of advanced materials

The potential of fuzzy sets and neural networks, often referred to as soft computing, for aiding in all aspects of manufacturing of advanced materials like ceramics is addressed. In design and manufacturing of advanced materials, it is desirable to find which of the many processing variables contribute most to the desired properties of the material. There is also interest in real time quality control of parameters that govern material properties during processing stages. The concepts of fuzzy sets and neural networks are briefly introduced and it is shown how they can be used in the design and manufacturing processes. These two computational methods are alternatives to other methods such as the Taguchi method. The two methods are demonstrated by using data collected at NASA Lewis Research Center. Future research directions are also discussed.

Cios, Krzysztof J.↗
NASA NTRS2003

Threshold Assessment of Gear Diagnostic Tools on Flight and Test Rig Data

A method for defining thresholds for vibration-based algorithms that provides the minimum number of false alarms while maintaining sensitivity to gear damage was developed. This analysis focused on two vibration based gear damage detection algorithms, FM4 and MSA. This method was developed using vibration data collected during surface fatigue tests performed in a spur gearbox rig. The thresholds were defined based on damage progression during tests with damage. The thresholds false alarm rates were then evaluated on spur gear tests without damage. Next, the same thresholds were applied to flight data from an OH-58 helicopter transmission. Results showed that thresholds defined in test rigs can be used to define thresholds in flight to correctly classify the transmission operation as normal.

Dempsey, Paula J.↗
NASA NTRS2005

Space Shuttle Transportation (Roll-Out) Loads Diagnostics

The Space Transportation System (STS) consists of three primary components; an Orbiter Vehicle, an External Fuel Tank, and two Solid Rocket Boosters. The Orbiter Vehicle and Solid Rocket Boosters are reusable components, and as such, they are susceptible to durability issues. Recently, the fatigue load spectra for these components have been updated to include load histories acquired during the rollout phase of the STS processing for flight. Using traditional program life assessment techniques, the incorporation of these "rollout" loads produced unacceptable life estimates for certain Orbiter structural members. As a result, the Space Shuttle System Engineering and Integration Office has initiated a program to re-assess the method used for developing the "rollout" loads and performing the life assessments. In the fall of 2003 a set of tests were preformed to provide information to either validate existing load spectra estimation techniques or generate new load spectra estimation methods. Acceleration and strain data were collected from two rollouts of a partial-stack configuration of the Space Shuttle. The partial stack configuration consists of two Solid Rocket Boosters tied together at the upper External Tank attachment locations mounted on the Mobile Launch Platform carried by a Crawler Transporter (CT). In the current analysis, the data collected from this test is examined for consistency in speed, surface condition effects, and the characterization of the forcing function. It is observed that the speed of the CT is relatively stable. The dynamic response acceleration of the partial-stack is slightly sensitive to the surface condition of the road used for transport, and the dynamic response acceleration of the partial-stack generally increases as the transport speed increases. However, the speed sensitivity is dependent on the measurement location. Finally, the character of the forcing function is narrow-banded with the primary drivers being harmonics of two CT speed dependent excitations. One source is an excitation due to the CT treads striking the road surface, and the second is unknown.

Elliott, Kenny B.↗
NASA NTRS2005

Geospatial Analysis and Remote Sensing from Airplanes and Satellites for Cultural Resources Management

Cultural resource management consists of research to identify, evaluate, document and assess cultural resources, planning to assist in decision-making, and stewardship to implement the preservation, protection and interpretation of these decisions and plans. One technique that may be useful in cultural resource management archaeology is remote sensing. It is the acquisition of data and derivative information about objects or materials (targets) located on the Earth's surface or in its atmosphere by using sensor mounted on platforms located at a distance from the targets to make measurements on interactions between the targets and electromagnetic radiation. Included in this definition are systems that acquire imagery by photographic methods and digital multispectral sensors. Data collected by digital multispectral sensors on aircraft and satellite platforms play a prominent role in many earth science applications, including land cover mapping, geology, soil science, agriculture, forestry, water resource management, urban and regional planning, and environmental assessments. Inherent in the analysis of remotely sensed data is the use of computer-based image processing techniques. Geographical information systems (GIS), designed for collecting, managing, and analyzing spatial information, are also useful in the analysis of remotely sensed data. A GIS can be used to integrate diverse types of spatially referenced digital data, including remotely sensed and map data. In archaeology, these tools have been used in various ways to aid in cultural resource projects. For example, they have been used to predict the presence of archaeological resources using modern environmental indicators. Remote sensing techniques have also been used to directly detect the presence of unknown sites based on the impact of past occupation on the Earth's surface. Additionally, remote sensing has been used as a mapping tool aimed at delineating the boundaries of a site or mapping previously unknown features. All of these applications are pertinent to the goals of site discovery and assessment in cultural resource management.

Giardino, Marco J.↗
NASA NTRS2006

A New Spin for Understanding the Peculiar Horizontal Branch Morphology of the Galactic Globular Clusters NGC 6388 and NGC 6441

In this paper we present multiband optical and UV Hubble Space Telescope photometry of the two Galactic globular clusters NGC 6388 and NGC 6441 Aims. We investigate the properties of their anomalous horizontal branches (HB) in different photometric planes in order to shed light on the nature of the physical mechanism(s) responsible for the existence of an extended HB blue tail, and of a slope in the HB, visible in all the color-magnitude diagrams. Methods. New photometric data have been collected and carefully reduced. Empirical data have been compared with updated stellar models of low-mass, metal-rich, He-burning structures, transformed to the observational plane with appropriate atmosphere models. Results. We have obtained the first UV color-magnitude diagrams for NGC 6388 and NGC 6441. These diagrams confirm previous results, obtained in optical bands, about the presence of a sizeable stellar population of extremely hot Horizontal Branch stars. At least in NGC 6388, we find a clear indication that at the hot end of the horizontal branch the distribution of stars forms a hook-like feature, closely resembling those observed in NGC 2808 and w Centauri. We briefly review the theoretical scenarios which have been suggested for interpreting this observational feature. We investigate also on the tilt in the horizontal branch morphology, and provide further evidence that supports early suggestions according to which this feature cannot be interpreted as an effect of differential reddening or radiative levitation, though these effects contribute to create the anomaly. We demonstrate that a possible solution of the puzzle is to assume that a small fraction (approx. 13% in NGC 6388 and approx. 8% NGC 6441) of the stellar population in the two clusters is strongly helium enriched (Y approx. 0.40 in NGC6388 and Y approx. 0.35 in NGC 6441). This solution necessarily implies the presence of a double generation of stars in the two clusters.

Busso, G.↗
NASA NTRS2020

An Overview of NASA’s Learn-to-Fly Technology Development

“Learn-to-Fly” (L2F) is an advanced technology development effort within the Modeling and Control for Agile Aircraft Development (MCAAD) group, a part of the NASA Transformative Aeronautics Concepts Program’s (TACP) Transformational Tools and Technologies (TTT) project. The goal of L2F is to develop technologies for enabling self-learning flight vehicles, using both ground- and flight-test approaches. Specifically, research has been conducted to demonstrate the potential to merge two enabling technologies, real-time aerodynamic modeling and adaptive flight controls, to substantially reduce the typical ground- and flight-testing time requirements for air vehicle design. This effort involved development of real-time, on-board algorithms to demonstrate key L2F technologies including aerodynamic modeling and control for an aircraft with minimal a priori knowledge. This research included both ground-based wind tunnel experiments utilizing modern design of experiments (DOE) techniques, as well as a flight test campaign, and was intended to rapidly advance these technologies and demonstrate key capabilities of the L2F approach. This paper presents the overview of the methods used to develop L2F technologies in NASA Langley Research Center’s 12-Foot Low Speed Tunnel, as well as in flight experiments. These methods include efficient wind tunnel data collection, real-time global nonlinear aerodynamic modeling in both the time and frequency domains, and the integration of the modeling techniques with adaptive flight controls, all of which were demonstrated using experimental validation.

Stephen E Riddick↗
NASA NTRSDate not supplied

Comparison of Dynamic Stability Testing Techniques with Magnetic Suspension Wind Tunnel

Dynamic stability testing techniques currently utilized at NASA Langley Research Center (LaRC) are conducted in multiple facilities and consists of free flight, forced oscillation, and free-to-oscillate tests. The MIT/NASA/ODU Magnetic Suspension and Balance System (MSBS) has been recommissioned to explore its utility as an additional facility to expand the dynamic stability test capabilities currently available at NASA LaRC. Simulations were created to replicate each current test facility and method as closely as possible. Data collected from the simulated environments was corrupted with replicated noise sources of the different testing environments and then compared to real data collected during tests when such data was available. The corrupted data was then passed through data reduction and System Identification (SID) to estimate the accuracy of the results with the known aerodynamic model that was utilized within the simulation to generate the original data. Magnitudes of noise were varied utilizing Monte Carlo analysis to perform sensitivity analysis of each noise source on the extracted dynamic stability coefficients. Some preliminary results will be presented.

Otoniel Ramirez↗
NASA NTRSDate not supplied

Comparison of Dynamic Stability Testing Techniques with Magnetic Suspension Wind Tunnel

Dynamic stability testing techniques currently utilized at NASA Langley Research Center (LaRC) are conducted in multiple facilities and consists of free flight, forced oscillation, and free-to-oscillate tests. The MIT/NASA/ODU Magnetic Suspension and Balance System (MSBS) has been recommissioned to explore its utility as an additional facility to expand the dynamic stability test capabilities currently available at NASA LaRC. Simulations were created to replicate each current test facility and method as closely as possible. Data collected from the simulated environments was corrupted with replicated noise sources of the different testing environments and then compared to real data collected during tests when such data was available. The corrupted data was then passed through data reduction and System Identification (SID) to estimate the accuracy of the results with the known aerodynamic model that was utilized within the simulation to generate the original data. Magnitudes of noise were varied utilizing Monte Carlo analysis to perform sensitivity analysis of each noise source on the extracted dynamic stability coefficients. Some preliminary results will be presented.

Otoniel Ramirez↗
DOE OSTI2021

U-10Mo Monolithic Fuel Qualification Plan

The Material Management and Minimization (M3) Program’s primary objective within the U.S. Department of Energy/National Nuclear Security Administration is to achieve permanent threat reduction by minimizing and when possible, eliminating weapon-usable nuclear material around the world. Under M3’s Office of Conversion, the U.S. High Performance Research Reactor (USHPRR) Project, is pursuing a fuel qualification and licensing effort focused on converting high-performance research reactors in the United States from using highly enriched uranium (HEU) fuel to using low-enriched uranium (LEU) fuel. The body of this document is focused on defining the activities required for generic qualification of high density, LEU fuel that then allows reactor conversions to proceed for the four high-performance research reactors and the critical assembly that will operate using the U-10Mo monolithic fuel design (see Subsection 1.3): MITR, MURR, NBSR, ATR, and ATRC. Fuel qualification in this report means generic fuel acceptance of the U-10Mo monolithic fuel form by the NRC for use in reactor conversions in the United States, which allows the fuel to be used in subsequent reactor specific licensing requests. The U 10Mo Monolithic Fuel Research, Development, and Qualification Plan utilizes the functions and requirements of the USHPRR Project that was established in the Functions and Requirements Document (F&RD) [1] and expands on these requirements to ensure that planned tests have traceable results that will ensure the requirement has been met. The methods by which data will be collected to show that these requirements have been met are described in this document.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Emulation automation and model checking

A method of automating emulations is provided. The method comprising collecting publicly available network data over a predefined time interval, wherein the collected network data might comprise structured and unstructured data. Any unstructured data is converted into structured data. The original and converted structured data is stored in a database and compared to known network vulnerabilities. An emulated network is created according to the collected network data and the comparison of the structured data with known vulnerabilities. Virtual machines are created to run on the emulated network. Director programs and guest actor programs are run on the virtual machines, wherein the actor programs imitate real user behavior on the emulated network. The director programs deliver task commands to the guest actor programs to imitate real user behavior. The imitated behavior is presented to a user via an interface.

Urias, Vincent↗